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Many-to-Many Hub Location-routing Problem with Robust Optimization Approach

Basirati, Mohadeseh | 2017

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 50154 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Akbari-Jokar, Mohammad Reza
  7. Abstract:
  8. Increasing the performance and efficiency of the transportation system is one of the issues that has attracted the attention of many managers and experts in the industrial sector during the last decades, which aims to deliver the product to the customer in the earliest possible time with minimum cost. Therefore, it appears crucial to shed light on two aspects of the issues in this regard. The first one is to create routes functioning as flow transmission interface between many origins and many destinations and to satisfy the allowable time of tours in each route. The other one is the route that vehicles must take within the window time of each destination point. It should be pointed that the aforementioned issues may create a substantial discrepancy in the cost of individual hubs and disturb the balance between the routes. In this thesis, a tri-objective model of many-to-many hub location and routing problem with hard time windows under the uncertainty circumstances has been presented. To this end, a robust optimization approach is adopted to resolve the uncertainty. The proposed model aims at keeping the workload balance of each vehicle including the distance and transported load, and simultaneously minimize the total transportation cost. Because of multi-objectivity and NP-Hard nature of the problem, a non-dominated sorting genetic algorithm (NSGA-II) has been employed to provide the Pareto-optimal solution to the problem. To demonstrate the efficiency of the proposed algorithm, a comparison is made between the Pareto-optimal results in the small and medium scales and those obtained from the exact multi-objective method, so-called ε-constraint. Finally, the performance of the proposed algorithm is assessed by making use of algorithm efficiency evaluation indices in order to extend the problem to large scales
  9. Keywords:
  10. Uncertainty ; Non-Dominate Sorting Genetic Algorithm (NSGAII) Method ; Epcilon-Constraint Method ; Work Load ; Balancing Strategy ; Hub Location Problem ; Many-to-Many Hub Location Routing Problem

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